Inequity Aversion and Individual Behavior in Public Good Games: An Experimental Investigation
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Transcript of Inequity Aversion and Individual Behavior in Public Good Games: An Experimental Investigation
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Astrid Dannenberg*, Thomas Riechmann**,
Bodo Sturm*, and Carsten Vogt***
*Centre for European Economic Research (ZEW) Mannheim
**Otto-von-Guericke-University Magdeburg
***Leipzig University of Applied Sciences
Supported by the German Research Foundation
ESA 2007 World Meeting, Rome
Inequity Aversion and Individual Behavior in Public Good Games:
An Experimental Investigation
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• Utility of subject i in a two-person game:
Objective of our study
• Low explanatory power of standard theory in social dilemmas
• to investigate the additional explanatory power of the Fehr
and Schmidt (1999) inequity aversion model
– αi ≥ 0 (aversion against disadvantageous inequality)
– βi ≥ 0 (aversion against advantageous inequality)
– βi < 1 and αi ≥ βi
• Assumptions:
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Experimental Design I
Games A and B (N = 492)• Modified ultimatum and dictator games (similar to Blanco et al. ´06)• Pure allocation games, i.e. no strategic interaction
• in order to elicit parameters αi and βi
Game D (N = 160)• Stage 1: as in Game C• Stage 2: punishment option with constant marginal costs
Step 1
Step 2
Game C (N = 160)• certain αi-βi-types were matched in pairs
• Standard two-player Public-Good game, Partner design, 10 periods
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Treatment βi, i = 1,2 Information Obs.
EGO βi < .3 yes 35
MIX β1 < .3 and β2 > .3 yes 13
FAIR βi > .3 yes 17
FAIR(ni) βi > .3 no 15
Treatment variables in Game C
• parameter βi
• information about co-player‘s type
Experimental Design II
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Hypotheses for Game C according to Fehr and Schmidt:
1. No contributions in EGO and MIX treatments
2. In FAIR, cooperation should be observed more frequently
than in EGO and MIX.
3. In FAIR, cooperation should be observed more frequently
than in FAIR(ni).
Experimental Design III
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Results: Games A&B
0 20 40 60 80percent
0(0;.1](.1;.2](.2;.3](.3;.4](.4;.5](.5;.6](.6;.7](.7;.8](.8;.9]
(.9;1.0)
0.2
.4.6
.81
be
ta
0 .5 1 1.5 2 2.5alfa
02
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0p
erc
en
t
0(0
;.2]
(.2
;.4]
(.4
;.6]
(.6
;.8]
(.8
;1.0
](1
.0;1
.2]
(1.2
;1.4
](1
.4;1
.6]
(1.6
;1.8
](1
.8;2
.0]
(2.0
;2.2
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.2;2
.4]
(2.4
;2.6
]
• No dispersion of αi
• Only 12% fulfill αi ≥ βi.
• Small negative correlation between βi and
studying economics
(Spearman‘s ρ = -0.137, p = 0.015)
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Results: Effect of βi in Game C
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1 2 3 4 5 6 7 8 9 10periods
mea
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FAIR EGO MIX
Last period
• Contributions: GFAIR > GEGO (MW U, p < 10%) and GFAIR > GMIX (MW U, p < 5%)
• H0 that cooperation and defection (G < 3€) have the same probability, has to be rejected
for FAIR, but not for EGO and MIX (Chi2, p < 5%).
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Results: Effect of Information in Game C
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1 2 3 4 5 6 7 8 9 10periods
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FAIR FAIR(ni) EGO
• Last period: Contributions in FAIR are significantly higher than in FAIR(ni) (MW U,
p < 5%). No difference between FAIR(ni) and EGO.
• No convergence between FAIR and FAIR(ni).
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Conclusions
• Specific composition of groups significantly influences the
subjects' performance in the PG games.
• Only parameter βi matters.
• As long as subjects are informed about the co-player’s type,
“fair” groups contribute more than “egoistic” or “mixed” groups.
• This information cannot be extracted during the PG game.
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Thank you for your attention!
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